2.a.guardian_nmf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n",
    "import datetime as datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import calendar\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raw_guardian = pd.read_pickle('news_dfs/new_guardian_df.pkl')\n",
    "guardian = raw_guardian[(raw_guardian.year == 2016)& (raw_guardian.month != 9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_df = 0.4, min_df = 15,\n",
    "                                   stop_words = 'english')\n",
    "tfidf = tfidf_vectorizer.fit_transform(guardian.article)\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n",
    "nmf = NMF(n_components = 52, random_state=1, alpha=.1, l1_ratio=.5).fit(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic:\" + str(int(topic_idx)+1))\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2396"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformed_data = nmf.transform(tfidf)\n",
    "len(transformed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Words per Topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic:1\n",
      "says family like photograph home world old day 000 life dont work city war british public say way did long\n",
      "Topic:2\n",
      "refugees greece turkey asylum eu refugee greek migrants europe border syrians seekers migration deal crisis syrian athens european germany 000\n",
      "Topic:3\n",
      "syrian syria aleppo assad talks al opposition ceasefire mistura geneva aid regime humanitarian civilians russia rebel russian damascus areas bashar\n",
      "Topic:4\n",
      "zika virus mosquito microcephaly health disease cases pregnant infected brazil mosquitoes infection spread symptoms brain babies transmission birth women outbreak\n",
      "Topic:5\n",
      "saudi arabia yemen arms nimr saudis shia al uk coalition riyadh sales export licences execution international law iran weapons military\n",
      "Topic:6\n",
      "children calais camp refugees unaccompanied uk child refugee jungle camps minors french migrants britain families help europe vulnerable amendment living\n",
      "Topic:7\n",
      "turkey turkish coup erdogan gulen turkeys ankara istanbul erdogans military kurdish pkk journalists recep attempt tayyip attempted kurds president crackdown\n",
      "Topic:8\n",
      "abortion abortions women clinics pregnancy ireland law health northern clinic access laws womans rights reproductive pregnancies providers services woman choice\n",
      "Topic:9\n",
      "drug drugs executions penalty death lethal marijuana narcotics execution states injection colombia heroin policy session cocaine executed use international crime\n",
      "Topic:10\n",
      "iran iranian sanctions nuclear tehran irans rouhani deal iranians revolutionary banks dual hassan guards ayatollah lifting tehrans foreign shia embassy\n",
      "Topic:11\n",
      "isis syria fighters raqqa islamic iraq al kurdish documents forces killed group syrian isiss video attack militants attacks fighting kurds\n",
      "Topic:12\n",
      "nato defence troops poland finland russia mission forces europe cooperation burns eastern nuclear european combat states sweden uk meeting summit\n",
      "Topic:13\n",
      "church churches anglican clergy england bishop bishops christian churchs welby christians archbishop catholic god canterbury faith abuse priest religion marriage\n",
      "Topic:14\n",
      "guzman el chapo del castillo penn mexican cartel guzmans sinaloa la drug chapos joaquin prison mexico los en kate extradition\n",
      "Topic:15\n",
      "irish gael fine ireland fail sinn fein easter kenny 1916 dublin taoiseach rising irelands seats election coalition labour enda party\n",
      "Topic:16\n",
      "brussels belgian abdeslam attacks paris belgium salah airport terrorist suicide metro police suspect bombers raid terror bombings french flat arrested\n",
      "Topic:17\n",
      "tax panama companies havens papers corruption avoidance offshore luxembourg taxes overseas territories evasion summit cameron ownership global beneficial international public\n",
      "Topic:18\n",
      "palestinian israel palestinians israeli israelis palestine bank peace israels settlement settlements west gaza territories jerusalem occupied land occupation stabbed embassy\n",
      "Topic:19\n",
      "pope francis vatican catholic polish abuse pontiff ii bishops papal popes pilgrims paul catholics poland visit john priests instagram mass\n",
      "Topic:20\n",
      "russian russia putin ukraine moscow russias kremlin putins vladimir ukrainian crimea sanctions soviet syria russians inquiry murder trump alexander sergei\n",
      "Topic:21\n",
      "china chinese chinas beijing sea hong kong xi south philippines communist tribunal islands vietnam territorial mainland rights claims military australia\n",
      "Topic:22\n",
      "israeli netanyahu israel jewish jews israels aviv tel arab gaza israelis jerusalem arabs rabbi prime military minister binyamin raid hostages\n",
      "Topic:23\n",
      "party podemos rajoy sanchez election ciudadanos coalition vote elections seats leader spain parties political spains majority form socialist polls votes\n",
      "Topic:24\n",
      "bst updated says 2016 11 court asks 10 april munich oscar june la en pic witness trump defence prison august\n",
      "Topic:25\n",
      "libya libyan tripoli gaddafi smugglers forces militias uae isis blunt unity military italian special troops italy oil migrants naval intervention\n",
      "Topic:26\n",
      "flight plane airport passengers egyptair aircraft crew crash board airlines pilot cairo crashed aviation egyptian planes pilots passenger airline air\n",
      "Topic:27\n",
      "puerto rico debt island islands senate creditors congress san rivera juan federal democrats crisis territory treasury garcia lopez debts supreme\n",
      "Topic:28\n",
      "pic twitter com 2016 july 12 29 gmt https august trump storm tweeted arrested snow photo digital reporter updated le\n",
      "Topic:29\n",
      "women muslim men gender womens female sexual woman cologne harassment religious muslims male feminist mosque feminism equality wear assault sex\n",
      "Topic:30\n",
      "zoo establish escalated escalating escalation escape escaped escaping escorted especially espionage essential essentially essex established erupted establishing establishment estate estimate\n",
      "Topic:31\n",
      "french france paris hollande frances le labour hollandes valls francois socialist charlie hebdo reforms strikes minister presidential unions union euro\n",
      "Topic:32\n",
      "rousseff impeachment lula dilma president brazil temer brazilian corruption brazils petrobras cunha party jato lava workers rousseffs silva coup investigation\n",
      "Topic:33\n",
      "dublin gang garda hotel irish boxing weigh gerry shot shooting gangs feud attack crime murder criminal dead christy gangsters north\n",
      "Topic:34\n",
      "gay lgbt sex transgender marriage pride gender rights homosexuality lesbian orlando parade trans community equality law bisexual anglican discrimination carolina\n",
      "Topic:35\n",
      "police officers attack man shot officer gun authorities munich shooting security violence arrested killed investigation gang gunman station dead men\n",
      "Topic:36\n",
      "falluja iraqi iraq city iraqs mosul baghdad shia al forces isis abadi militias sunni saddam ramadi military kurdish sectarian iraqis\n",
      "Topic:37\n",
      "burkini ban beach burkinis bans wearing nice french muslim wear cannes headscarf dress mayors banned clothing mayor headscarves france religious\n",
      "Topic:38\n",
      "italian egyptian egypt cairo italy egypts rome sisi cambridge murder torture investigation italys renzi abdel student authorities death case tortured\n",
      "Topic:39\n",
      "taliban pakistan afghan afghanistan kabul pakistani peace drone pakistans killed attack strike leader khan mohammad militants omar afghans army forces\n",
      "Topic:40\n",
      "cuban obama castro cuba havana gmt raul obamas visit president barack speech embargo american trip rights relations congress revolution white\n",
      "Topic:41\n",
      "data powers bulk privacy surveillance communications legislation security charter agencies records information web law safeguards internet intelligence services access qc\n",
      "Topic:42\n",
      "university students student college universities columbia prisoners madrid campus professor khan california bangladesh cambridge oxford political academics hossein indian school\n",
      "Topic:43\n",
      "quake earthquake italy rubble buildings survivors rescue towns magnitude italys del rescuers italian renzi struck seismic hit town collapsed alive\n",
      "Topic:44\n",
      "zoo animal animals species park wild captivity wildlife conservation captive toronto cat staff escaped cats traps lure welfare carter captured\n",
      "Topic:45\n",
      "eu european brexit uk europe referendum cameron britain vote commission leave trade brussels union british deal member juncker countries parliament\n",
      "Topic:46\n",
      "maduro venezuela venezuelas recall opposition referendum president shortages nicolas signatures chavez inflation oil assembly food electricity emergency power fridays socialist\n",
      "Topic:47\n",
      "nice truck attack bouhlel bastille driver crowd france french terrorist fireworks 84 des mohamed day vehicle saw tunisian lorry drove\n",
      "Topic:48\n",
      "trudeau canadian canada canadas justin minister prime ontario indigenous nations toronto conservative brown canadians incident federal liberals commons mps driver\n",
      "Topic:49\n",
      "fort alberta wildfire fires flames evacuation city blaze residents smoke oil sands hectares firefighters homes size winds burned raging weather\n",
      "Topic:50\n",
      "trial court sexual case assault charges judge prosecutors allegations sentence verdict lawyer justice alleged prison hearing guilty evidence testimony witness\n",
      "Topic:51\n",
      "mexico pena nieto mexican trump mexicos mexicans students enrique city investigation federal states president wall 43 fox 31 cartel trumps\n",
      "Topic:52\n",
      "korea north kim korean nuclear koreas pyongyang test south weapons congress treaty bomb il tests missile china obama military policy\n",
      "()\n"
     ]
    }
   ],
   "source": [
    "print_top_words(nmf, tfidf_feature_names, 20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Labels for Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "topics = {1:'Other', 2:'EU Refugee Crisis',\n",
    "         3:'Syria',4:'Zika',\n",
    "          5: 'Saudi Arabia', 6: 'EU Refugee Crisis',\n",
    "          7: 'Turkey Coup', 8: \"Women's Rights\",\n",
    "          9: 'Drugs', 10: 'Iran',\n",
    "          11: 'ISIS', 12: 'NATO',\n",
    "          13: 'Christianity', 14: 'El Chapo',\n",
    "          15: 'Ireland', 16: \"Brussels Attack\",\n",
    "          17: 'Panama Papers', 18: 'Israel/Palestine',\n",
    "          19: 'Pope Francis', 20: 'Russia',\n",
    "          21: 'China', 22: 'Israel',\n",
    "          23: 'Spanish Government Deadlock', 24: 'Other',\n",
    "          25: 'Libya', 26: \"EgyptAir Crash\",\n",
    "         27: 'Puerto Rico Debt', 28: 'Other', \n",
    "         29: 'Muslim Women Rights', 30: 'Other',\n",
    "         31: 'France', 32: 'Brazil Corruption',\n",
    "         33: 'Doublin: Romulus Shooting', 34: 'Orlando Shootings',\n",
    "         35: 'Police' , 36: 'Iraq', \n",
    "         37: 'Burkini Ban' , 38: 'Egypt: Italian Student Death', \n",
    "         39: 'Taliban', 40: 'Cuba',\n",
    "         41: 'Surveillance', 42: 'Schools',\n",
    "         43: 'Earthquakes', 44: 'Zoo',\n",
    "         45: 'Brexit', 46: 'Venezuela',\n",
    "         47: 'Nice Attack', 48: 'Canada', \n",
    "         49: 'Fort McMurray Fire', 50: 'Trials',\n",
    "         51: 'Mexico', 52: 'North Korea'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "final_data = pd.DataFrame(transformed_data).applymap(lambda x: 1 if x>0.2 else 0)\n",
    "final_data.columns = topics.values()\n",
    "final_data['month'] = [i for i in guardian.month]\n",
    "\n",
    "by_month = final_data.groupby('month').sum()\n",
    "for i in range(8):\n",
    "    by_month.iloc[i] = by_month.iloc[i]/float(np.sum(by_month.iloc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_month.to_pickle('guardian_month.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "by_month = pd.read_pickle('guardian_month.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Topic per Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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wAKUMAADAAJQyAAAAA1DKAAAADEApAwAAMAClDAAAwACUMgAAAANQygAAAAxA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1135a5b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "rc={'xtick.labelsize': 10, 'ytick.labelsize': 10, 'axes.labelsize': 10, 'font.size': 10, 'legend.fontsize': 30.0, 'axes.titlesize': 10, \"figure.figsize\": [10, 5]}\n",
    "sns.set(rc = rc)\n",
    "by_month.iloc[0].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for January')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HDpbq2YduUHx8N7OjAADOEKUMttGidaxcUR3NjgEAwEkxpwwAAMACKGUAAAAW4LdSZhiG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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114250910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[1].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for February')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xYxo1apSeeOIJ3XTTTbXaTnHx6cuNWmtFRQ6fbcsoRUUOHT1aYnSMeiEiIoTf0o8xfv6L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\nzVVMTIxnWXR0tA4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WmTNnNHfuXEncVN7MPvnkEy1btswzB7Bt27Z6/vnnlZycTCnzA5s2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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11463af10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[2].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for March')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yrc57X5aWlmrkyJGy2+3q1auXqqurfZELAAAgoNTrhuQFBQWSpP3793PdHAAAwAvqLGUz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\nWSm3263S0lK99dZbVsdCPR0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114276d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[3].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for April')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0tDRJUnV1tVJTU5WRkaHAwEAlJCRo1KhROnDggD7++GOlp6frzJkzWrVqlbfiAQAAmIrX\nSlleXp6GDx8uSerfv78KCgo8Y4WFhYqMjJTNZpMkDRo0SLt379a///1v9enTR/fdd5+cTqd+97vf\neSseAACAqXitlDkcDgUFBf13R1aramtr5efnd95Yx44d5XA4VFZWpq+++kovvPCCjh49qnvvvVf/\n+Mc/LrifLl06ympt5623UUdZma1F9mOk0FCbwsKCGn6gj2HufBvzh6bis/RtbXX+vFbKbDabnE6n\n5/a5QnZuzOFweMacTqeCg4PVuXNnRUdHy2q1KioqSoGBgSotLVVoaGi9+ykrO+Ott3Ce0lJHww/y\ncaWlDpWUVBgdo9kxd76N+UNThIUF8Vn6sNY+fxcqnF779mVMTIx27twpScrPz1efPn08Y9HR0Soq\nKlJ5eblcLpf27NmjAQMGaODAgXr//fclScePH9fZs2fVpUsXb0UEAAAwDa8dKRszZoxycnIUHx8v\nSUpJSVFmZqYqKytlt9uVmJioGTNmyO12Ky4uTuHh4QoPD9eePXsUFxcnt9utBQsWyGKxeCsiAACA\naXitlFksFi1cuLDOfVFRUZ7t2NhYxcbGnve8hx9+2FuRAAAATIuLxwIAAJiA146UAQAgSTU1NTp8\n+GCL7a+szNaiXw7p1au32rVrmasAoHWjlAEAvOrw4YOa/dSb6hgSbnSUZnfmdLFWPDJJ0dE/MjoK\nWgFKGQDA6zqGhMvWpYfRMQBTY00ZAACACVDKAAAATIBSBgAAYAKUMgAAABOglAEAAJgApQwAAMAE\nKGUAAAAmQCkDAAAwAUoZAACACVDKAAAATIBSBgAAYAKUMgAAABOglAEAAJgApQwAAMAEKGUAAAAm\nYG3oAQ6HQ3/9619VXFysESNGqG/fvoqMjGyJbAAAAG1Gg0fKHnvsMUVERKioqEjdunXTvHnzWiIX\nAABAm9JgKTt16pTi4uJktVoVExOj2tralsgFAADQpjRqTVlhYaEk6ZtvvlG7du28GggAAKAtanBN\n2bx58/TYY4+psLBQs2bN0oIFCxr1wm63W0lJSdq/f78CAgK0ePFiRUREeMazs7OVlpYmq9WqqVOn\nym63S5Juuukm2Ww2SVLPnj21ZMmSi3lfAAAAPqXBUta3b19t2LChyS+clZUll8ul9PR07d27Vykp\nKUpLS5MkVVdXKzU1VRkZGQoMDFRCQoJGjRrlKWNr1qxp8v4AAAB8WYOlbOTIkbJYLJ7bNptNW7Zs\nafCF8/LyNHz4cElS//79VVBQ4BkrLCxUZGSkp4QNHDhQubm5uvTSS3XmzBnNnDlTNTU1mjNnjvr3\n79/kNwUAAOBrGixl//jHPyR9dzqyoKDAc7shDodDQUFB/92R1ara2lr5+fmdN9apUydVVFSod+/e\nmjlzpux2uw4fPqy77rpL77zzjvz86l/61qVLR1mtLbPOrazM1iL7MVJoqE1hYUENP9DHMHe+jfnz\nba19/lrz3BmlrX6eDZaygIAAz/bAgQP1zDPPNOqFbTabnE6n5/a5QnZuzOFweMacTqeCg4MVGRmp\nyy+/XJLUq1cvde7cWSUlJerevXu9+ykrO9OoPM2htNTR8IN8XGmpQyUlFUbHaHbMnW9j/nxba5+/\n1jx3RggLC2rVn+eFCmeDpWzZsmWe05fFxcUXPGr1fTExMXrvvfc0duxY5efnq0+fPp6x6OhoFRUV\nqby8XO3bt9eePXs0c+ZMvf766/riiy+0YMECHT9+XE6nU2FhYY3aHwAAgC9rsJT17t3bs33llVd6\n1ok1ZMyYMcrJyVF8fLwkKSUlRZmZmaqsrJTdbldiYqJmzJght9utuLg4hYeHKy4uTomJibrlllvk\n5+enJUuWNLoEAgAA+LJ6S9kHH3wgSecdqdq7d6+uu+66Bl/YYrFo4cKFde6LiorybMfGxio2NrbO\nuL+/v55++ukGXxsAAKC1qbeUbd26td4nNaaUAQAA31ZTU6PDhw+26D7Lymwtug6xV6/eprkwfr2l\nLCUl5QfvLy4u9loYAABgHocPH9Tsp95Ux5Bwo6N4xZnTxVrxyCRFR//I6CiSGrGmbMWKFVq/fr2q\nqqp09uxZ9erV64JH0QAAQOvRMSRcti49jI7RJjS4ij47O1u7du3SxIkT9dZbb13w8hQAAAC4OA2W\nsrCwMAUEBMjpdCoyMlJVVVUtkQsAAKBNabCUXXLJJdq0aZM6dOigZcuWqby8vCVyAQAAtCkNrilL\nTk7W119/rbFjx2rz5s1atmxZS+QCAABoU+otZTfccIMmT56suLg49ejx3QK/6dOnt1gwAACAtqTe\n05fp6enq0KGD7rrrLv32t7/Vzp07WzIXAABAm1JvKQsNDdUdd9yhLVu26O6771Z2dramTJmitLS0\nlswHAADQJjS4pkyS+vXrp9raWlksFm3ZskX33Xeft3MBAAC0KRcsZceOHdMbb7yht99+W71799bN\nN9+sBQsWtFQ2AACANqPeUnbbbbfpxIkTiouL0+rVq9W1a9eWzAUAANCm1FvKHnjgAQ0ZMqQlswAA\nALRZ9S70p5ABAAC0nAav6A8AAADvq7eUffrppy2ZAwAAoE2rt5Q99dRTnu1Fixa1SBgAAIC2qt5S\n5na7PdtffPFFi4QBAABoq+otZRaLpSVzAAAAtGn1XhLj+PHj2rBhg9xut2f7nGnTprVIOAAAgLai\n3lI2ceJElZSUnLcNAACA5ldvKbv//vtbMgcAAECbVm8pGzly5HnrytxutywWi7Zv397gC7vdbiUl\nJWn//v0KCAjQ4sWLFRER4RnPzs5WWlqarFarpk6dKrvd7hk7efKkpk6dqpdeeklRUVEX874AAAB8\nygVLWUFBgX7+859r0qRJuuyyy5r0wllZWXK5XEpPT9fevXuVkpKitLQ0SVJ1dbVSU1OVkZGhwMBA\nJSQkaNSoUQoNDVV1dbUWLFig9u3b/2/vDAAAwIfU++3L+fPn69VXX9WAAQOUlpamBx54QFu2bJHL\n5WrUC+fl5Wn48OGSpP79+6ugoMAzVlhYqMjISNlsNvn7+2vgwIHKzc2VJP3hD39QQkKCwsPD/5f3\nBQAA4FPqPVImSX5+frr++ut1/fXX69SpU0pKStKiRYu0d+/eBl/Y4XAoKCjovzuyWlVbWys/P7/z\nxjp16qSKigpt3rxZXbt21bBhw/T888836g106dJRVmu7Rj32f1VWZmuR/RgpNNSmsLCghh/oY5g7\n38b8+bbWPn/MnW8z0/xdsJTV1tYqJydHW7du1eeff67rr79eGzdubNQL22w2OZ3OOq/l5+fnGXM4\nHJ4xp9Op4OBgrV27VpKUk5Ojffv26dFHH9Vf/vIXde3atd79lJWdaVSe5lBa6mj4QT6utNShkpIK\no2M0O+bOtzF/vq21zx9z59taev4uVADrLWVJSUnas2ePBg8erJtvvlkxMTFN2mlMTIzee+89jR07\nVvn5+erTp49nLDo6WkVFRSovL1f79u2Vm5urmTNn6oYbbvA8Zvr06UpOTr5gIQMAAGgt6i1l6enp\n6ty5s7Zt26Zt27bVGfvggw8afOExY8YoJydH8fHxkqSUlBRlZmaqsrJSdrtdiYmJmjFjhtxut+x2\n+3lryPhFAQAA0JbUW8r27dv3P72wxWLRwoUL69z3/ctbxMbGKjY2tt7nr1mz5n/aPwAAgC+pt5S9\n8cYb9T5p8uTJXgkDAADQVtVbygoLC+vcrq2t1ebNm9W+fXtKGQAAQDOrt5TNnTvXs33kyBE9+uij\nio2N1WOPPdYiwQAAANqSC14SQ5LWrVun1atXKzExUSNGjGiJTAAAAG1OvaXs+PHjSkxMVEhIiDZu\n3KiQkJCWzAUAANCm1FvKJkyYoICAAF177bVKTk6uM7Zs2TKvBwMAAGhL6i1l5348HAAAAN5Xbykb\nPHhwS+YAAABo0/yMDgAAAABKGQAAgClQygAAAEyAUgYAAGAClDIAAAAToJQBAACYAKUMAADABChl\nAAAAJkApAwAAMAFKGQAAgAlQygAAAEyAUgYAAGAClDIAAAAToJQBAACYAKUMAADABKzeemG3262k\npCTt379fAQEBWrx4sSIiIjzj2dnZSktLk9Vq1dSpU2W321VbW6v58+fr0KFD8vPz08KFC3XFFVd4\nKyIAAIBpeO1IWVZWllwul9LT0zV37lylpKR4xqqrq5WamqqXX35Za9eu1YYNG1RaWqrs7GxZLBat\nX79es2fP1jPPPOOteAAAAKbitSNleXl5Gj58uCSpf//+Kigo8IwVFhYqMjJSNptNkjRw4EDl5ubq\nl7/8pUaOHClJOnbsmEJCQrwVDwAAwFS8VsocDoeCgoL+uyOrVbW1tfLz8ztvrFOnTqqoqJAk+fn5\n6fe//72ysrL0pz/9qcH9dOnSUVZru+Z/Az+grMzWIvsxUmioTWFhQQ0/0Mcwd76N+fNtrX3+mDvf\nZqb581ops9lscjqdntvnCtm5MYfD4RlzOp0KDg723E5NTdXJkydlt9v11ltvqX379vXup6zsjBfS\n/7DSUkfDD/JxpaUOlZRUGB2j2TF3vo35822tff6YO9/W0vN3oQLotTVlMTEx2rlzpyQpPz9fffr0\n8YxFR0erqKhI5eXlcrlc2rNnjwYMGKAtW7Zo5cqVkqTAwED5+fl5ihwAAEBr5rUjZWPGjFFOTo7i\n4+MlSSkpKcrMzFRlZaXsdrsSExM1Y8YMud1uxcXFKTw8XDfccIMSExN12223qbq6WvPmzVNAQIC3\nIgIAAJiG10qZxWLRwoUL69wXFRXl2Y6NjVVsbGyd8Q4dOmj58uXeigQAAGBanBsEAAAwAUoZAACA\nCVDKAAAATIBSBgAAYAKUMgAAABOglAEAAJgApQwAAMAEKGUAAAAmQCkDAAAwAUoZAACACVDKAAAA\nTIBSBgAAYAKUMgAAABOglAEAAJgApQwAAMAEKGUAAAAmQCkDAAAwAUoZAACACVDKAAAATIBSBgAA\nYAKUMgAAABOglAEAAJgApQwAAMAErN56YbfbraSkJO3fv18BAQFavHixIiIiPOPZ2dlKS0uT1WrV\n1KlTZbfbVV1drccee0zHjh1TVVWV7rnnHo0cOdJbEQEAAEzDa6UsKytLLpdL6enp2rt3r1JSUpSW\nliZJqq6uVmpqqjIyMhQYGKiEhASNGjVKO3bsUJcuXbR06VKdPn1akydPppQBAIA2wWulLC8vT8OH\nD5ck9e/fXwUFBZ6xwsJCRUZGymazSZIGDhyo3NxcjRs3TmPHjpUk1dbWymr1WjwAAABT8VrrcTgc\nCgoK+u+OrFbV1tbKz8/vvLFOnTqpoqJCHTp08Dx39uzZmjNnToP76dKlo6zWds3/Bn5AWZmtRfZj\npNBQm8LCghp+oI9h7nwb8+fbWvv8MXe+zUzz57VSZrPZ5HQ6PbfPFbJzYw6HwzPmdDoVHBwsSfr6\n6691//3367bbbtP48eMb3E9Z2ZlmTl6/0lJHww/ycaWlDpWUVBgdo9kxd76N+fNtrX3+mDvf1tLz\nd6EC6LVvX8bExGjnzp2SpPz8fPXp08czFh0draKiIpWXl8vlcik3N1cDBgzQiRMnNHPmTD3yyCOa\nMmWKt6IBAACYjteOlI0ZM0Y5OTmKj4+XJKWkpCgzM1OVlZWy2+1KTEzUjBkz5Ha7ZbfbFR4ersWL\nF6u8vFxpaWl67rnnZLFY9Le//U0BAQHeigkAAGAKXitlFotFCxcurHNfVFSUZzs2NlaxsbF1xufN\nm6d58+Z5KxIAAIBpcfFYAAAAE6CUAQAAmAClDAAAwAQoZQAAACZAKQMAADABShkAAIAJUMoAAABM\ngFIGAABgApQyAAAAE6CUAQAAmAClDAAAwAQoZQAAACZAKQMAADABShkAAIAJUMoAAABMgFIGAABg\nApQyAAAAE6CUAQAAmAClDAAAwAQoZQAAACZAKQMAADABShkAAIAJUMoAAABMwGulzO12a8GCBYqP\nj9ftt9+uo0eP1hnPzs5WXFyc4uPjtXHjxjpje/fu1fTp070VDQAAwHSs3nrhrKwsuVwupaena+/e\nvUpJSVFaWpokqbq6WqmpqcrIyFBgYKASEhI0atQohYaG6m9/+5u2bNmiTp06eSsaAACA6XjtSFle\nXp6GDx/TnSv8AAAZFklEQVQuSerfv78KCgo8Y4WFhYqMjJTNZpO/v78GDhyo3NxcSVJkZKSee+45\nb8UCAAAwJa8dKXM4HAoKCvrvjqxW1dbWys/P77yxTp06qaKiQpI0ZswYHTt2rNH76dKlo6zWds0X\n/ALKymwtsh8jhYbaFBYW1PADfQxz59uYP9/W2uePufNtZpo/r5Uym80mp9PpuX2ukJ0bczgcnjGn\n06ng4OCL2k9Z2Zn/LWgTlJY6Gn6QjystdaikpMLoGM2OufNtzJ9va+3zx9z5tpaevwsVQK+dvoyJ\nidHOnTslSfn5+erTp49nLDo6WkVFRSovL5fL5VJubq4GDBhQ5/lut9tb0QAAAEzHa0fKxowZo5yc\nHMXHx0uSUlJSlJmZqcrKStntdiUmJmrGjBlyu92y2+0KDw+v83yLxeKtaAAAAKbjtVJmsVi0cOHC\nOvdFRUV5tmNjYxUbG/uDz+3Ro4fS09O9FQ0AAMB0uHgsAACACVDKAAAATIBSBgAAYAKUMgAAABOg\nlAEAAJgApQwAAMAEKGUAAAAmQCkDAAAwAUoZAACACVDKAAAATIBSBgAAYAKUMgAAABOglAEAAJgA\npQwAAMAEKGUAAAAmQCkDAAAwAUoZAACACVDKAAAATIBSBgAAYAKUMgAAABOglAEAAJgApQwAAMAE\nKGUAAAAmYPXWC7vdbiUlJWn//v0KCAjQ4sWLFRER4RnPzs5WWlqarFarpk6dKrvd3uBzAAAAWiuv\nHSnLysqSy+VSenq65s6dq5SUFM9YdXW1UlNT9fLLL2vt2rXasGGDSktLL/gcAACA1sxrR8ry8vI0\nfPhwSVL//v1VUFDgGSssLFRkZKRsNpskadCgQdq9e7fy8/PrfY5ZnDldbHQEr2nN701q3e+vNb+3\nc1rze2zN7+2c1voeW+v7+r7W/B7N9t68VsocDoeCgoL+uyOrVbW1tfLz8ztvrGPHjqqoqJDT6az3\nOfUJCwuqd6y5hYXF6P9ej2mx/aH5MHe+jfnzbcyf72LuWpbXTl/abDY5nU7P7e+XK5vNJofD4Rlz\nOp0KCQm54HMAAABaM681npiYGO3cuVOSlJ+frz59+njGoqOjVVRUpPLycrlcLu3Zs0cDBgzQNddc\nU+9zAAAAWjOL2+12e+OFv/9NSklKSUnRZ599psrKStntdu3YsUPPPvus3G634uLilJCQ8IPPiYqK\n8kY8AAAAU/FaKQMAAEDjsWALAADABChlAAAAJkApAwAAMAFKGQAAgAl47eKxQFt26tQpde7c2egY\nAOAzTp48qW+//dZz+7LLLjMwjTEoZSazY8cOvfrqqzp79qznvjVr1hiYCE2xe/duJScnq6amRmPH\njtVll10mu91udCw0wWeffVbnH4aYGK5m7iueffbZesfuv//+FkyCpkpKStKuXbsUHh4ut9sti8Wi\n9PR0o2O1OEqZyaxYsUKJiYnq1q2b0VFwEVasWKFXXnlFDzzwgO655x4lJCRQynzIrFmzdPz4cXXv\n3l2SZLFYKGU+pKioSB06dNCQIUNUUFCgzz//XGPHjjU6Fhrhk08+UVZWVpv/FR9KmcmEhIRo8ODB\nRsfARfLz81Pnzp1lsVgUGBioTp06GR0JTXD8+HFt2LDB6Bi4SCdOnNBLL70kSZowYYJmzJih+Ph4\ng1OhMSIjI/Xtt9+qQ4cORkcxFKXMJM79Q+Dv76/HH39cV199tSwWiyRp2rRpRkZDE1x++eVatmyZ\nTp06pZUrV7bJNRG+rHfv3jpx4gRHqn3U6dOnVVRUpMjISBUWFtZZBgJz+/rrrzVixAhFRkZKUps9\nfckV/U2CtRCtg8vl0uuvv64vvvhCvXv31rRp0xQQEGB0LDTSuHHjdPToUXXt2lXSd/8w7Nixw9hQ\naLTc3FwlJyerrKxMPXv2VFJSkq688kqjY6ERjh07dt59PXr0MCCJsShlJuRwOCRJWVlZGjFihEJC\nQgxOhMaaMWOGVq1aZXQMXKSamprz7mvXrp0BSdAcqqqq5O/vb3QMNMI333yjJUuWqLCwUL169VJi\nYqJ69uxpdKwWx+lLk5kzZ45iY2P18ccfq7a2Vu+++66ee+45o2OhkYKDg7V9+3b16tXLs2A1KirK\n4FRorM8++0ybN29WdXW13G63iouLtXLlSqNjoZHS09P10ksveebParVq27ZtRsdCI8yfP18JCQn6\n2c9+pt27d2vevHlavXq10bFaXNv+moMJFRcX61e/+pUKCwuVnJwsp9NpdCQ0wcmTJ/Xyyy8rKSlJ\nTzzxhBYsWGB0JDTBE088of79+6u0tFRdu3ZVUFCQ0ZHQBOvWrdPatWt1/fXXKyUlRVdccYXRkdBI\n3377rUaNGqXg4GCNHj1a1dXVRkcyBEfKTKaqqkrbtm3TFVdcodLSUkqZj1m7dq3REfA/6Ny5syZP\nnqz/+7//05w5c3TbbbcZHQlNEB4ervDwcDmdTg0ZMuSCa3VhLjU1Ndq/f7/69u2r/fv3e77o1tZQ\nykzmzjvv1NatW5WYmKi1a9fqvvvuMzoSGmHWrFn605/+pOuuu+68sQ8++MCARLgY7dq1U2FhoSor\nK3XkyBGdPn3a6EhogqCgIGVlZXm+uXfq1CmjI6GR5s+fr8cee0zFxcXq3r27nnzySaMjGYKF/iZX\nXFys8PBwo2MAbcL+/ft14MABde/eXcnJyfrVr36lmTNnGh0LjeRwOHTkyBF17dpVL730kkaMGKEh\nQ4YYHQtoNEqZySxfvlzp6emqqqrS2bNn1atXL23dutXoWGjAuSNlkrRz50794he/MDgRLlZhYaEO\nHTqkK664Qr169TI6DpqAbz/7Hs4y1MXpS5N57733tGvXLi1ZskR33HGHFi5caHQkNEJZWZln+8UX\nX6SU+agXXnhB2dnZ+slPfqKVK1dq0qRJrCvzIcHBwcrKylJUVBTffvYR5/6Y3bhxoy699FLP/YWF\nhUZFMhSlzGTCwsIUEBAgp9OpyMhIVVVVGR0JTcTBZ9+VnZ2t9evXy8/PT9XV1brlllsoZT7k5MmT\nWrNmTZ37/v/bMJcvvvhCx48f19NPP63f/e53crvdqq2t1bJly7Rlyxaj47U4SpnJXHLJJdq0aZM6\ndOigZcuWqby83OhIaKSqqipPIfv+Nlf09x2hoaE6e/asOnbsqOrqanXp0sXoSGiEadOmyWKxnPcH\nUVv9Bp8vKS8v11tvvaWTJ08qMzNT0nfzdssttxiczBisKTOZ06dPy+FwKCQkRJs3b9bQoUO51o4P\nGDlypOcfALfb7fkHwmKxaPv27QanQ0NuueUWWSwWnThxQpWVlbrqqqv05ZdfqnPnzsrIyDA6Hhrw\nQz/Rc05b/KkeX/TZZ5/pqquu8lwjsK0WakqZySQkJGj9+vVGxwDalCNHjtQ7dvnll7dgEqBt2rZt\nm1JTUxUSEiKHw6GkpCQNGzbM6FgtjtOXJhMSEqLVq1fXWaj6Q99KAdB8cnNzNXXqVC1fvvy8v9Bn\nz55tUCqg7UhLS9PGjRvVtWtXnThxQvfccw+lDMbr0qWL9u3bp3379nnuo5QB3tWtWzdJnOoCjNK5\nc2d17dpV0nf/PdpsNoMTGYPTlyZy6tQpde7cWZJUUlKidu3aKTQ01OBUaIpPP/1UP/3pT42OgSb6\n17/+Ve/Y0KFDWzAJ0Db99re/1dmzZ/Wzn/1Mn332mUpKSjR48GBJ0kMPPWRwupbDkTKT2L17tx59\n9FG98cYbCgkJ0f79+/X444/rqaee0qBBg4yOh0ZatWqVjh07pkmTJmnSpEkKDg42OhIaob7F/BaL\nhVIGtIDRo0d7trt3725gEmNxpMwkbrnlFi1dulQ9e/b03Hfo0CHNmzdPr776qoHJ0FSnT59WZmam\nsrKyFBoaqptvvpmfevFRpaWlHK0GWkBubu559/3sZz8zIImxOFJmEu3atatTyCTVWewP33HixAl9\n9dVXKisrU3R0tN555x1t3LhRTz/9tNHR0IBnn31W69atU3V1tSorKxUREaG3337b6FhAq3fuqgNu\nt1sHDhxQjx49KGUwzrmrGH+/hNXU1HBFfx9jt9vVvn172e12zZ4923PhWH7U2je8++672rFjh1JT\nUzV9+nQtXrzY6EhAm/DMM894tl0ulx588EED0xiHUmYSkyZN0kMPPaR77rlHPXv21DfffKO0tDSN\nGzfO6GhogqeeeuoHf8T6xRdfbPkwaLKwsDAFBgbK4XCod+/ecrlcRkcC2pyamhodPXrU6BiGoJSZ\nxM033yybzaYlS5aouLhYPXr00NSpUzV+/Hijo6EJCgsL9eSTT3p+ZunUqVP6+9//bnQsNFJ4eLgy\nMjLUvn17LV++nJ85A1rI9y/9VF1drV//+tcGpjEOC/2BZjRx4kQlJycrPT1dQ4YM0T//+U/WkvmA\nBx98UMuXL1dNTY2++uordenSRZs2bdLQoUPVt29fo+MBrd6//vUvvuksiVXkQDMKDw/XNddcI0m6\n6aabdPz4cYMToTFKS0slffeFm4iICNlsNv3mN7+hkAEt5NlnnzU6gilw+hJoRv7+/srNzVV1dbXe\nf/99lZWVGR0JjXD06NE6C42/ry1duBIwisVi0W9/+9s6Vx1oi//tUcpMZu7cuVq2bJnRMXCRFi5c\nqIMHD+ree+/VihUrdO+99xodCY3Qvn17RUVFGR0DaLMmT56sdu3aGR3DcJQyk3G5XNq3b5+ioqI8\nP4x87rIKML+lS5d6SvWf//xng9Ogsbp166YpU6YYHQNos9566y2tWrXK6BiGo5SZzKFDh3Tfffd5\nblssFm3fvt3ARGgKSrVv+slPfmJ0BKBNCw4OVlZWVp3Tl23x6DXfvgSa0cSJE+V0OmWxWOR2uynV\nANAI06dPr3PbYrFozZo1BqUxDqXMJJKTk/XEE09o2rRpniMs56SnpxuUCk21ZcsW/epXvzI6BgD4\nnLKyMh09elQ9e/Zss785SykziRMnTqhbt246duxYnfu/+eYbDRw40KBUaKrbbrtNr7zyitExAMCn\nvP3221q+fLmio6P15Zdf6v7772+Tf+BSykzqww8/1Lp16/TRRx8pJyfH6DhopJtvvlkul6vOugi+\nTQsAFzZt2jStWrVKnTp1ksPh0K9//Wu9/vrrRsdqcSz0N5EzZ85o8+bNWr9+vUpKSvT444/zD7qP\nefjhh42OAAA+x2KxqFOnTpIkm82mwMBAgxMZg1JmEk8++aQ+/PBDjR49Ws8++6wWLVqkG2+80ehY\naKKvvvrK6AgA4HMiIiKUmpqqQYMGac+ePbr88suNjmQISplJ5OXl6eqrr1b//v11+eWXn7fYH76h\nsLBQkuR2u/X555+rc+fOmjx5ssGpAMDcUlJStGHDBv3zn/9UdHS05s6da3QkQ7CmzEQ++ugjbdy4\nUXl5eXK73Xr++ecVHR1tdCxcJLfbrbvvvlsrV640OgoAmNYHH3ygYcOGyWKx6PPPP1dJSYmuv/56\no2MZgiNlJhITE6OYmBg5HA69+eabeuSRRyRJGRkZBidDY7lcLs92SUmJ/vOf/xiYBgDM7dVXX9Wb\nb76pAQMGyGazyWKx6LnnntPXX3+tadOmGR2vxXGkzGTee+89jRgxwnP7hRde0N13321gIjTFyJEj\nPReObd++vWbOnKmpU6caHQsATMlut+uVV16ps7Df6XTq9ttv59uXMM57772njz76SFu3btXHH38s\nSaqtrdX27dspZT4kOzvb6AgA4DPat29/3jctO3Xq5PkmZltDKTOJK6+8UqdOnVJgYKDn974sFosm\nTJhgcDI0xu23317vWFv8qRAAaAx/f3+VlpbWuYJ/aWmpampqDExlHE5fmkxtba3279+vQ4cOKTo6\nWn379jU6Ehrhnnvu0ZEjRzRu3DiNHj26zl9+vXv3NjAZAJjXnj17tHjxYk2ePFkRERH6+uuvtWnT\nJj3yyCP6+c9/bnS8FkcpM5nly5frww8/VL9+/fTJJ59o9OjRuvPOO42OhUY4deqU3n77bW3fvl1h\nYWGaOHGihg4dyuVNAOACjh49qi1btqi4uFg9evTQjTfeqB49ehgdyxCUMpO56aabtGnTJvn5+amm\npkbTpk3Tpk2bjI6FJjp27JieeuopffTRR9q1a5fRcQAAPoA1ZSbTvXt3OZ1OBQUFqbq6Wt26dTM6\nEprg4MGD2rp1q7KzsxUVFaWFCxcaHQkA4CM4UmYycXFx+uqrr3TllVfqwIED8vf3V1hYmCQpPT3d\n4HSoz1//+ldt27ZNXbt21YQJEzR69Gh16NDB6FgAAB9CKTOZQ4cOKSAg4AfH2uo5dl9w5ZVX6vLL\nL1eXLl0kqc46Mso0APywDRs21DvWFi8ey+lLk5k1a5auvfZa2e129enTx+g4aKTt27cbHQEAfE5J\nSYnREUyFI2UmU1tbq/fff1+vv/66ysrKNGnSJI0fP77NXkjP12RnZ6ugoECzZs3SzJkzdccdd+i6\n664zOhYAmNI333yjSy65RIcOHTpv7Nw1O9sSSpkJud1u7dq1S5s2bVJRUZE6duyoG2+8UbfddpvR\n0dCAKVOmaM2aNQoKClJFRYXuuusuTl8CQD1SUlKUmJio6dOne5Z9uN1uWSyWNnnhbU5fmszSpUu1\nfft2DR48WHfddZf69eun2tpa3XTTTZQyH2C1WhUUFCRJCgoKkp+fn8GJAMC8EhMTJUmzZ8/WoEGD\nPPdnZmYaFclQlDKT6dWrlzIyMuqcrvTz89Ozzz5rYCo0Vr9+/TR37lwNGDBAn3zyiX784x8bHQkA\nTO83v/mN7rrrLs2ePVuS9Nprr+nGG280OFXLo5SZzMcff+z5QXLpu98Fu+SSS3TrrbcamAqN9fjj\njysrK0sHDx7UuHHjNHLkSKMjAYDpXXPNNaqpqdHjjz+u5ORko+MYhnMrJvPtt98qPDxc48ePV48e\nPXT8+HG5XC49+uijRkfDBbzxxhue/zkcDoWHh6u8vFxvvPGG0dEAwPQsFoseeughRUVF6YEHHlBb\nXe7OkTKTKS0t1TPPPCNJGj58uGbMmKEHH3yQI2UmN3/+fF122WUaMWKEAgMD2+z/oQDAxbDZbJKk\nGTNmKCgoSElJScYGMgilzGQcDocKCwsVHR2twsJCOZ1OlZWV6cyZM0ZHwwXs2rVLW7du1Y4dO3Tp\npZdq4sSJGjJkiNGxAMAnpKWlebbtdruGDRtmYBrjcEkMk/nkk0+UlJSk4uJiXXrppXriiSf0ySef\nqFu3bvrlL39pdDw0wpEjR/Tmm2/qo48+0tVXX625c+caHQkATC09PV0vvfSSqqur5Xa75e/vr3fe\necfoWC2OI2Um069fP2VkZHhuu1wu/fSnPzUwEZrKz89P/v7+cjgcKioqMjoOAJjeunXrtHbtWv3l\nL3/R2LFjtXr1aqMjGYKF/ibx4IMPerZXrVrl2b7zzjuNiIMmKikp0Zo1a5SQkKAFCxYoLCxMq1at\n0p/+9CejowGA6YWHhys8PFxOp1NDhgxRRUWF0ZEMQSkziZMnT3q2d+zY4dnm7LJv+MUvfqENGzZo\n2LBhGjNmjKqqqrR169YL/tguAOA7QUFBysrKksViUXp6uk6dOmV0JENw+tKEvl/Ezv3sBMzt3nvv\n9czViRMnDE4DAL5l0aJFOnLkiB566CG99NJLmj9/vtGRDEEpM4nvly+KmO954IEHjI4AAD5r1qxZ\nnqU7v//97w1OYxxKmUkcOHBAc+fOldvtrrNdWFhodDQAALwqODhYWVlZioqK8vxmcFRUlMGpWh6X\nxDCJ3bt31zs2ePDgFkwCAEDLmj59ep3bFotFa9asMSiNcShlQDNKTk7WE0884bn9u9/9TkuXLjUw\nEQD4DpfLJUkKCAgwOIkxOH0JNIN169bpL3/5i06dOqVt27Z57o+OjjYwFQCY2759+7R8+XJ17dpV\nEyZM0Jw5cyRJiYmJmjx5ssHpWh5HyoBm9Pzzz+uee+4xOgYA+IT4+Hg98MADOn36tObNm6fNmzcr\nNDRUd955p1577TWj47U4rlMGNKOdO3caHQEAfIa/v7+GDRum8ePHq2/fvurVq5eCg4PVsWNHo6MZ\ngtOXQDMKCQnR6tWr63yD6LrrrjM4FQCY0/cvAfX9dWS1tbVGxDEcpQxoRl26dNG+ffu0b98+z32U\nMgD4YVwOqi7WlAHN7IsvvtCBAwcUFRWlq666yug4AGBaXA6qLkoZ0IzWrl2rzMxM9evXTx9//LHG\njRunmTNnGh0LAOADKGVAM5o2bZrWrVsnq9WqqqoqxcfH6/XXXzc6FgDAB/DtS6AZud1uWa3fLdX0\n9/eXv7+/wYkAAL6Chf5AMxo4cKBmzZqlgQMHKi8vT9dcc43RkQAAPoLTl0Az27FjhwoLCxUdHa3Y\n2Fij4wAAfASlDGgGaWlpuu+++yRJxcXFCg8PNzgRAMDXsKYMaAYffvihZ/vhhx82MAkAwFdRyoBm\n8P0Dzhx8BgBcDEoZ0Ay+/1Mh398GAKCxWFMGNIOBAwfqRz/6keenQs5tWywWpaenGx0PAOADKGVA\nMzh27Fi9Yz169GjBJAAAX0UpAwAAMAHWlAEAAJgApQwAAMAEKGUAAAAmQCkDAAAwgf8HdMmcQ52v\n8wMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114027390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[4].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for May')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AADABShkAAIAJUMoAAABMgFIGAABgApQyAAAAE6CUAQAAmAClDAAAwAQoZQAAACZAKQMA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\n61x4zJIYnsNisejo0aPKz8/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RSk1NVXJyshYvXqxu3bopKSnJ6GiAV9iwYYOSkpJUrVo1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\n/D+FQktjNZ9hwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1140d4990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[5].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for June')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TNGnSJE9FAgAAMC1uHgsAAGAClDIAAAAToJQBAACYAKUMAADABChlAAAAJkApAwAAMAFK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\nm/lLz8f07NlTpaWl+uCDD7R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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ad4810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[6].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for July')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dya+//qqxY8dqxIgRuuqqq5pcPjIyTFZru2PK2lyVlTa/bMdIUVE2RUeHN71ggGHscDT4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\n0vRWOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141dfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# El Chapo\n",
    "# Orlando Shooting\n",
    "by_month.iloc[7].sort_values(ascending = False).head().plot(kind = 'bar')\n",
    "plt.ylabel('NMF Value')\n",
    "_ = plt.title('Topics for August')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Topics for the Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X8AAAAAAAAAAAAAANwUSX8AAAAAAAAAAAAAANzU/wNV\nP+APaQaXygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112872ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', \"June\", \"July\", 'Aug']\n",
    "\n",
    "lst = []\n",
    "topics_ =[]\n",
    "for i in range(8):\n",
    "    x = by_month.iloc[i].sort_values(ascending = False).head(1)\n",
    "    lst.append(x[0]*100)\n",
    "    topics_.append(x.keys()[0])\n",
    "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', \"Jun\", \"July\", 'August']\n",
    "rc={'xtick.labelsize': 30, 'ytick.labelsize': 30, 'axes.labelsize': 25, 'font.size': 25, 'legend.fontsize': 30.0, 'axes.titlesize': 20, \"figure.figsize\": [35, 14]}\n",
    "sns.set(rc = rc)\n",
    "sns.barplot(x = months, y = lst)\n",
    "plt.title('Guardian Top Topics Per Month', fontsize=40)\n",
    "plt.xlabel('Months', fontsize=35)\n",
    "_ = plt.ylabel('% of Articles', fontsize=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Saudi Arabia',\n",
       " 'Zika',\n",
       " 'Brussels Attack',\n",
       " 'Syria',\n",
       " 'EgyptAir Crash',\n",
       " 'Orlando Shootings',\n",
       " 'Turkey Coup',\n",
       " 'Burkini Ban']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Comparing with Other Countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NYT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nyt = pd.read_pickle('nyt_month.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "months = ['Jan', 'Feb', 'Mar', 'Apr', \"May\", \"June\", 'July', 'Aug']\n",
    "nyt_lst = []\n",
    "nyt_topics_ =[]\n",
    "for i in range(8):\n",
    "    x = nyt.iloc[i].sort_values(ascending = False).head(1)\n",
    "    nyt_lst.append(x[0])\n",
    "    nyt_topics_.append(x.keys()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113689210>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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qNojanHri6+DgYPTu3bvJ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\nPCMiIiIiIiIiIiIiIiL6z/8B4Uo/BXJUi3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11366ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = months, y = nyt_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Saudi Arabia',\n",
       " 'Syria',\n",
       " 'EU Refugee Crisis',\n",
       " 'Panama Papers',\n",
       " 'Brazil Corruption',\n",
       " 'Brexit',\n",
       " 'Turkey Coup',\n",
       " 'Rio Olympics']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt_topics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nyt_df = pd.DataFrame(nyt_lst, index = nyt_topics_ , columns = ['nyt'])\n",
    "guardian_df = pd.DataFrame(lst, index = topics_ , columns = ['guardian'])\n",
    "joint_df = nyt_df.join(guardian_df, how = 'inner')\n",
    "melt_df = pd.melt(joint_df)\n",
    "melt_df['month'] = ['Jan', 'Feb', 'July']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1141c3710>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qs2HDBly7dk16kN2tWzfs3r0b165dw+nTpxEYGIjLly/j2LFj+Oyzz6CioiLt6+/vj6tX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\n0njljWlra4u//voL/fr1k/6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10405bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'month', y = 'value', data = melt_df, hue = 'variable')\n",
    "plt.title('Guardian-NYT Joint Articles', fontsize=40)\n",
    "plt.xlabel('Months', fontsize=35)\n",
    "plt.ylabel('% of Articles', fontsize=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nyt</th>\n",
       "      <th>guardian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <td>0.070833</td>\n",
       "      <td>0.153153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syria</th>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.097087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey Coup</th>\n",
       "      <td>0.089194</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   nyt  guardian\n",
       "Saudi Arabia  0.070833  0.153153\n",
       "Syria         0.090909  0.097087\n",
       "Turkey Coup   0.089194  0.160000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "republica = pd.read_pickle('republica_month.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "republica_lst = []\n",
    "republica_topics_ =[]\n",
    "for i in range(8):\n",
    "    x = republica.iloc[i].sort_values(ascending = False).head(1)\n",
    "    republica_lst.append(x[0])\n",
    "    republica_topics_.append(x.keys()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114ecc450>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xxRcrxzd1ndp99903+3zQEu69996s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\nAAAAAMBXLJ4BAAAAAAAAAAAAX/0PcVyEpJ9wdIQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ed5b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = months, y = republica_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['El Chapo',\n",
       " 'Pope Francis',\n",
       " 'Brazil Corruption',\n",
       " 'Ecuador Earthquake',\n",
       " 'Brazil Corruption',\n",
       " 'Brexit',\n",
       " 'Turkey Coup',\n",
       " 'U.S. Presidential Election']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "republica_topics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "republica_df = pd.DataFrame(republica_lst, index = republica_topics_ , columns = ['republica'])\n",
    "joint_df_republica_nyt = nyt_df.join(republica_df, how = 'inner')\n",
    "joint_df_republica_nyt = joint_df_republica_nyt.iloc[1:]\n",
    "republica_nyt_melt_df = pd.melt(joint_df_republica_nyt)\n",
    "republica_nyt_melt_df['month'] = ['Apr', 'May', 'June']*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115631890>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5uSEwMBDe3t64efMm9u7di0aNGkkPz4OCgnIcbvTMmTM4ePCgtA8WFhb47bffcOvWLVy9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\nqU/liSeeyAMPPJChQ4fm6quvTpMmTXLzzTfnb3/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1155c5b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'month', y = 'value', data = republica_nyt_melt_df, hue = 'variable')\n",
    "plt.title('Republica-NYT Joint Articles', fontsize=40)\n",
    "plt.xlabel('Months', fontsize=35)\n",
    "plt.ylabel('% of Articles', fontsize=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nyt</th>\n",
       "      <th>republica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil Corruption</th>\n",
       "      <td>0.079417</td>\n",
       "      <td>0.234146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brexit</th>\n",
       "      <td>0.152216</td>\n",
       "      <td>0.135747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey Coup</th>\n",
       "      <td>0.089194</td>\n",
       "      <td>0.165803</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nyt  republica\n",
       "Brazil Corruption  0.079417   0.234146\n",
       "Brexit             0.152216   0.135747\n",
       "Turkey Coup        0.089194   0.165803"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint_df_republica_nyt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Saudi Arabia',\n",
       " 'Syria',\n",
       " 'EU Refugee Crisis',\n",
       " 'Panama Papers',\n",
       " 'Brazil Corruption',\n",
       " 'Brexit',\n",
       " 'Turkey Coup',\n",
       " 'Rio Olympics']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nyt_topics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "anaconda-cloud": {},
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   "language": "python",
   "name": "conda-env-datasci27-py"
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  "language_info": {
   "codemirror_mode": {
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